Efficient Unsupervised Temporal Segmentation of Human Motion

Abstract

This work introduces an efficient method for fully automatic temporal segmentation of human motion sequences and similar time series. The method relies on a neighborhood graph to partition a given data sequence into distinct activities and motion primitives according to self-similar structures given in that input sequence. In particular, the fast detection of repetitions within the discovered activity segments is a crucial problem of any motion processing pipeline directed at motion analysis and synthesis. The same similarity information in the neighborhood graph is further exploited to cluster these primitives into larger entities of semantic significance. The elements subject to this classification are then used as prior for estimating the same target values for entirely unknown streams of data.

The technique makes no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. Tests of our techniques are conducted on the CMU and HDM05 motion capture databases demonstrating the capability of our system handling motion segmentation, clustering, motion synthesis and transfer-of-label problems in practice - the latter being an optional step which relies on the preexistence of a small set of labeled data.

Images

Download Paper

Additional Material

Bibtex

@INPROCEEDINGS{voegele2014a,
author = {V{\"o}gele, Anna and Kr{\"u}ger, Bj{\"o}rn and Klein, Reinhard},
title = {Efficient Unsupervised Temporal Segmentation of Human Motion},
booktitle = {2014 ACM SIGGRAPH/Eurographics Symposium on Computer Animation},
year = {2014},
month = jul,
location = {Copenhagen, Denmark},
abstract = {This work introduces an efficient method for fully automatic temporal segmentation of human motion
sequences and similar time series. The method relies on a neighborhood graph to partition a given
data sequence into distinct activities and motion primitives according to self-similar structures
given in that input sequence. In particular, the fast detection of repetitions within the discovered
activity segments is a crucial problem of any motion processing pipeline directed at motion analysis
and synthesis. The same similarity information in the neighborhood graph is further exploited to
cluster these primitives into larger entities of semantic significance. The elements subject to this
classification are then used as prior for estimating the same target values for entirely unknown
streams of data.
The technique makes no assumptions about the motion sequences at hand and no user interaction is
required for the segmentation or clustering. Tests of our techniques are conducted on the CMU and
HDM05 motion capture databases demonstrating the capability of our system handling motion
segmentation, clustering, motion synthesis and transfer-of-label problems in practice - the latter
being an optional step which relies on the preexistence of a small set of labeled data.}
}